Quantitative analysis equipment is used to analyze a sample substance (analyte) to determine its chemical composition. The equipment is generally specialized to measure certain chemicals or to analyze specific substances. The equipment includes chemical analysis equipment used in the chemical industry and medical diagnostic equipment in the medical industry.
In the medical industry, for example, diagnostic equipment is used to analyze patient fluid samples, such as blood samples. Such medical diagnostic equipment can measure properties such as blood gases, electrolytes, and glucose levels. Based on the analysis, physicians and other health care providers diagnose illnesses and implement treatments. To obtain effective treatment, it is therefore important that the medical diagnostic equipment function correctly and provide an accurate analysis.
In an effort to maintain accuracy, the analysis equipment should be routinely tested. Typically, quality control samples of known compositions are analyzed by the equipment. Based on the equipment's measurements of the quality control sample, the equipment may be declared to be operating within or outside its acceptable range of operation. This decision, however, must typically be based on accurate quality control data entry. In reality, human data entry errors can adversely affect the decision process.
Although raw data measurements of quality control samples can be automatically validated by each individual piece of equipment, statistical analysis of the equipment's performance over time relies on cumulative quality control measurement data. This cumulative data typically includes measurements, by the particular unit, of quality control samples over time. The statistics from any one piece of equipment may also be compared with selected groups of similar equipment, such as peer groups. Again, human data entry errors can make such comparisons unreliable.
Furthermore, error checking within peer groups typically involves the use of broad error checking parameters based solely upon the analyte being tested. Typically, a peer analysis program requires delivery of quality control data to a dedicated remote technology for error checking. This dedicated remote technology may include a database having error functionality.
The quality control data can be delivered electronically or manually inputted. Typically, the quality control data is delivered in the form of internal summary data or internal raw data (for example, Levy-Jennings charts) that is manually typed into the database. The quality control data is then checked against global analyte parameters.
Results of the data checking are sent to a central database where the results are printed and manually checked for obvious errors. Typically, outlier data is either included in the report and the database, or excluded entirely.